scholarly journals Learning-based classification of multispectral images for deterioration mapping of historic structures

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Efstathios Adamopoulos
Keyword(s):  
Low Cost ◽  
Spectral Band ◽  
Detailed Knowledge ◽  
Automated Mapping ◽  
Supervised Segmentation ◽  
Historic Structures ◽  
Band Combinations ◽  
Non Destructive

AbstractThe conservation of historic structures requires detailed knowledge of their state of preservation. Documentation of deterioration makes it possible to identify risk factors and interpret weathering mechanisms. It is usually performed using non-destructive methods such as mapping of surface features. The automated mapping of deterioration is a direction not often explored, especially when the investigated architectural surfaces present a multitude of deterioration forms and consist of heterogeneous materials, which significantly complicates the generation of thematic decay maps. This work combines reflectance imaging and supervised segmentation, based on machine learning methods, to automatically segment deterioration patterns on multispectral image composites, using a weathered historic fortification as a case study. Several spectral band combinations and image classification techniques (regression, decision tree, and ensemble learning algorithmic implementations) are evaluated to propose an accurate approach. The automated thematic mapping facilitates the spatial and semantic description of the deterioration patterns. Furthermore, the utilization of low-cost photographic equipment and easily operable digital image processing software adds to the practicality and agility of the presented methodology.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals Classification of soybean tempe quality using deep learning

2021 ◽  
Vol 924 (1) ◽  
pp. 012022
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
I Prakoso ◽  
V Liana ◽  
M R Fauzy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Low Cost ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Traditional Food ◽  
Quality Classification ◽  
Non Destructive

Abstract Tempe is a traditional food originating from Indonesia, which is made from the fermentation process of soybean using Rhizopus mold. The purpose of this study was to classify three quality levels of soybean tempe i.e., fresh, consumable, and non-consumable using a convolutional neural network (CNN) based deep learning. Four types of pre-trained networks CNN were used in this study i.e. SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The sensitivity analysis showed the highest quality classification accuracy of soybean tempe was 100% can be achieved when using AlexNet with SGDm optimizer and learning rate of 0.0001; GoogLeNet with Adam optimizer and learning rate 0.0001, GoogLeNet with RMSProp optimizer, and learning rate 0.0001, ResNet50 with Adam optimizer and learning rate 0.00005, ResNet50 with Adam optimizer and learning rate 0.0001, and SqueezeNet with RSMProp optimizer and learning rate 0.0001. In further testing using testing-set data, the classification accuracy based on the confusion matrix reached 98.33%. The combination of the CNN model and the low-cost digital commercial camera can later be used to detect the quality of soybean tempe with the advantages of being non-destructive, rapid, accurate, low-cost, and real-time.

Case Study: Text-Mining Customers View Point and Perceived Value About Brand

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Madhumita Ghosh
Keyword(s):  
Text Mining ◽  
Association Rule ◽  
Low Cost ◽  
Perceived Value ◽  
View Point ◽  
Human Effort ◽  
Brand Perception ◽  
Consumer Voice

paper describes how text mining techniques can be applied in the analysis of consumer voice to gain useful and actionable business insights for marketers. The technique is illustrated via its application to understand Brands perceived value of certain automobile brands. This case study shows the use of text mining techniques to understand brands perception vis-a-vis competition from their opinion, sentiment and reactions. As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Data acquisition in this case is not costly, information is rich in nature, classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper, we mention about a procedure of classifying text using the concept of association rule of data mining and correspondence analysis for Brand perception.

Application of Conventional UAVs for the Identification and Classification of Dense Green Spaces

2021 ◽  
pp. 364-388
Author(s):  
Josep Roca ◽  
Blanca Arellano
Keyword(s):  
Plant Species ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Urban Spaces ◽  
Green Spaces ◽  
Spatial Density ◽  
Public Parks

The objective of this chapter is to show the usefulness of conventional UAVs for the identification, inventory, and classification of trees in the context of dense green spaces. The aim is to demonstrate the potential of low-cost drones (with traditional red, green, blue [RGB] sensors) to identify and classify trees in public parks. A case study is discussed on Turó Parc in Barcelona, in which a 3D model was developed and an exercise to identify and classify the vegetation was carried out using the information provided by a UAV. The example confirms that conventional drones could be useful for studying green urban spaces characterized by a high density of plant species. Non-professional UAVs have a potential that should not be undervalued, as they enable three-dimensional point clouds to be obtained of high spatial density.

scholarly journals Classification of large green chilli maturity using deep learning

2021 ◽  
Vol 924 (1) ◽  
pp. 012009
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
I Prakoso ◽  
V Liana ◽  
M R Fauzy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Small Molecules ◽  
Classification Accuracy ◽  
Low Cost ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Maturity Index ◽  
Maturity Stage ◽  
Non Destructive

Abstract Chili (Capsicum annuum L.) is the source of various nutraceutical small molecules, such as ascorbic acid (vitamin C), carotenoids, tocopherols, flavonoids, and capsinoids. The purpose of this study was to classify the maturity stage of large green chili into three maturity levels, i.e. maturity 1 (maturity index 1 / 34 days after anthesis (DAA)), maturity 2 (maturity index 3 / 47 DAA), and maturity 3 (maturity index 5 / 60 DAA) by using convolutional neural networks (CNN) based deep learning and computer vision. Four types of pre-trained networks CNN were used in this study i.e. SqueezeNet, GoogLeNet, ResNet50, and AlexNet. From the overall sensitivity analysis results, the highest maturity classification accuracy of large green chili was 93.89% which can be achieved when using GoogLeNet with SGDmoptimizer and learning rate of 0.00005. However, in further testing using testing-set data, the highest classification accuracy based on confusion matrix was reaching 91.27% when using the CNN SqueezeNet model with RMSProp optimizer and a learning rate of 0.0001. The combination of the CNN model and the low-cost digital commercial camera can later be used to detect the maturity of large green chili with the advantages of being non-destructive, rapid, accurate, low-cost, and real-time.

scholarly journals POTENTIAL OF NON-CALIBRATED UAV-BASED RGB IMAGERY FOR FORAGE MONITORING: CASE STUDY AT THE RENGEN LONG-TERM GRASSLAND EXPERIMENT (RGE), GERMANY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 203-206 ◽  
Author(s):  
G. Bareth ◽  
U. Lussem ◽  
J. Menne ◽  
J. Hollberg ◽  
J. Schellberg
Keyword(s):  
Spatial Data ◽  
Vegetation Index ◽  
Low Cost ◽  
Floristic Composition ◽  
Growing Period ◽  
Spatio Temporal ◽  
Non Destructive ◽  
Term Field

<p><strong>Abstract.</strong> Forage monitoring in grassland is an important task to support management decisions. Spatial data on (i) yield,(ii) quality, and (iii) floristic composition are of interest. The spatio-temporal variability in grasslands is significant and requires fast and low-cost methods for data delivery. Therefore, the overarching aim of this contribution is the investigation of low-cost and non-calibrated UAV-derived RGB imagery for forage monitoring. Study area is the Rengen Grassland Experiment (RGE) in Germany which is a long-term field experiment since 1941. Due to the experiment layout, destructive biomass sampling during the growing period was not possible. Hence, non-destructive Rising Plate Meter (RPM) measurements, which are a common method to estimate biomass in grasslands, were carried out. UAV campaigns with a Canon Powershot 110 mounted on a DJI Phantom 2 were conducted in the first growing season in 2014. From the RGB imagery, the RGB vegetation index (RGBVI) and the Grassland Index (GrassI) introduced by Bendig et al. (2015) and Bareth et al. (2015), respectively, were computed. The RGBVI and the GrassI perform very well against the RPM measurements resulting in R<sup>2</sup> of 0.84 and 0.9, respectively. These results indicate the potential of low-cost UAV methods for grassland monitoring and correspond well to the studies of Viljanen et al. (2018) and Näsi et al. (2018).</p>

Failure Analysis Case Study of a 1.5 Meter Space Flex Harness

2017 ◽  
Author(s):  
Erick Kim ◽  
Kamjou Mansour ◽  
Gil Garteiz ◽  
Javeck Verdugo ◽  
Ryan Ross ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Failure Modes ◽  
Field Of View ◽  
Area Of Interest ◽  
Air Stream ◽  
Novel Method ◽  
Temperature Controlled ◽  
Non Destructive ◽  
Failure Site

Abstract This paper presents the failure analysis on a 1.5m flex harness for a space flight instrument that exhibited two failure modes: global isolation resistances between all adjacent traces measured tens of milliohm and lower resistance on the order of 1 kiloohm was observed on several pins. It shows a novel method using a temperature controlled air stream while monitoring isolation resistance to identify a general area of interest of a low isolation resistance failure. The paper explains how isolation resistance measurements were taken and details the steps taken in both destructive and non-destructive analyses. In theory, infrared hotspot could have been completed along the length of the flex harness to locate the failure site. However, with a field of view of approximately 5 x 5 cm, this technique would have been time prohibitive.

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